CXCL17

前言肿瘤是一个复杂的生态系统，其中癌症细胞和多种宿主细胞之间的相互作用影响疾病的进展和治疗反应。在肿瘤进展过程中，癌症细胞采用多种途径来逃避免疫攻击，例如下调抗原提呈机制或诱导抑制性免疫检查点分子，而免疫压力在肿瘤的发展和转移扩散过程中促进克隆进化。同时，癌症细胞劫持免疫细胞，如中性粒细胞、巨噬细胞和调节性T细胞（Treg），以协调免疫抑制性肿瘤微环境（TME）。这反过来促进免疫逃逸，促进血管和细胞外基质的重塑，并支持癌症进展和治疗抵抗。异常免疫反应被广泛认为是癌症的标志，并为癌症治疗提供了有吸引力的靶点。因此，我们有必要深入了解癌症细胞的内在特征，包括遗传变异、信号通路调节和代谢改变，它们在协调免疫景观的组成和功能状态方面发挥关键作用，并影响免疫调节策略的治疗效益。遗传改变癌基因、抑癌基因或DNA损伤修复基因中的特定癌症细胞固有遗传改变有助于调节癌症免疫状况。癌基因癌症细胞中不同的致癌基因通过不同的机制调节免疫行为，这些机制因癌症类型、癌症分期和癌症部位而异。例如，KRAS突变通过IL-8诱导的炎症、IL-6介导的巨噬细胞和Treg细胞浸润、GM-CSF诱导的Gr1+CD11b+髓系细胞的扩增、IL-10和TGF-β介导的CD4+CD25−T细胞向Treg细胞的转化、以及抑制干扰素调节因子2（IRF2）和随后产生的CXCL3，从而导致CD8+T细胞抑制，髓系衍生抑制细胞（MDSCs）的迁移增强。MYC通过增强CD47和PD-L1表达来抑制抗肿瘤免疫，从而削弱巨噬细胞和T细胞的募集。此外通过分泌IL-1β，阻断细胞毒性T细胞、NK细胞和树突状细胞的浸润，并增强支持肿瘤的巨噬细胞和中性粒细胞的浸润。KRAS突变和MYC还通过与MYC相互作用的锌指蛋白1（MIZ1）产生协同作用，介导IFN-β的抑制以及CCL9和IL-23的分泌，增强PD-L1+巨噬细胞浸润，抑制B细胞、T细胞和NK淋巴细胞驱动免疫逃避。此外，突变的表皮生长因子受体（EGFR）通过降低PD-L1表达来驱动免疫逃逸，通过IRF1介导的CXCL10下调阻碍CD8+T细胞募集，并通过CD73依赖性腺苷生成或JNK–JUN介导的CCL2上调促进Treg细胞浸润。人表皮生长因子受体2（HER2）扩增导致主要组织相容性复合物I类（MHC-I）的下调和TANK结合激酶1（TBK1）的磷酸化，其抑制下游cGAS–STING信号传导和IFNβ产生，导致免疫逃避。抑癌基因抑癌基因通过不同的环境依赖机制调节免疫。例如，研究发现Trp53的缺失通过WNT配体介导的巨噬细胞极化和IL-1β的产生驱动免疫抑制，导致中性粒细胞的全身聚集，从而CD8+T细胞的抑制和CXCL17、CCL3、CCL11、CXCL5和M-CSF介导的巨噬细胞和Treg细胞的募集。TP53的突变与PD-L1表达相关，并通过CXCL2分泌调节免疫抑制中性粒细胞的募集，通过NF-κB介导的IL-8调节慢性炎症，并且结合TBK1抑制下游cGAS–STING–IRF3信号传导和IFN-β1的产生，其减少T细胞和NK细胞浸润并增强巨噬细胞极化。相反，TP53的功能增益突变与新抗原表达，以及IFN-γ和CXCL9表达相关，这支持抗肿瘤免疫。在KRAS突变肿瘤中，丝氨酸/苏氨酸激酶11（STK11）的失活通过粒细胞集落刺激因子（G-CSF）、IL-6和CXCL7的表达刺激免疫抑制中性粒细胞的积聚。此外，KRAS和STK11突变肿瘤中PD-L1的表达会导致对免疫检查点阻断（ICB）的抵抗。最后，PTEN丢失通过CCL2和VEGF的分泌减少CD8+T细胞浸润，并激活PI3K信号增强PD-L1的表达，以及NF-κB介导的CCL20、CXCL1、IL-6和IL-23分泌增强Treg细胞和髓系细胞浸润，并通过IL-1β和M-CSF驱动局部MDSC扩增。DNA损伤修复机制的遗传改变癌症细胞中的错配修复（MMR）缺陷会导致突变、新抗原和细胞中DNA的积累，从而触发cGAS–STING依赖性的T细胞浸润，免疫检查点分子如PD-1、PDL1、CTLA-4和LAG-3在浸润免疫细胞上的表达增强。在BRCA1突变的肿瘤中，双链DNA积聚在细胞中，并引发与CD8+T细胞浸润相关的cGAS–STING介导的炎症反应。cGAS–STING还促进免疫抑制性髓系细胞、巨噬细胞、Treg细胞和耗竭的PD1+T细胞的募集，以及VEGF的上调。DNA传感基因和下游介质（如IFN-β或CCL5）的遗传缺失或表观遗传沉默可介导BRCA1突变肿瘤的免疫逃逸，以及PD-L1的表达增强。相反，BRCA2突变肿瘤显示cGAS–STING介导的各种T细胞群的富集。表观遗传修饰除了遗传改变外，表观遗传改变也是癌症细胞的一个共同特征，在核结构和基因转录中起着至关重要的作用。癌症表观基因组会发生各种变化，包括DNA甲基化、组蛋白修饰、染色质重塑和非编码RNA的失调。越来越多的证据表明，癌症细胞中发生的表观遗传变化也会影响与免疫系统的串扰。DNA甲基化由于超级增强子（SE）的形成，炎症基因的DNA去甲基化导致CXC趋化因子配体（CXCL）介导的中性粒细胞募集，而总体低甲基化与PD-L1、IL-6和VEGF的表达增强相关。而DNA（超）甲基化可降低编码PD-L1的基因CD274和编码MHC-I的HLA基因的表达，而编码cGAS–STING的基因通过启动子甲基化的转录抑制增强MHC-I表达和T细胞识别。异柠檬酸脱氢酶1（IDH1）或IDH2的突变通过将α-酮戊二酸转化为（R）-2-羟基戊二酸（2HG）来诱导全局超甲基化。而编码PD-L1、CXCL9和CXCL10免疫相关基因的转录抑制会损害IDH突变肿瘤中CD8+T细胞的浸润，G-CSF的转录激活会增强非抑制性中性粒细胞和前中性粒细胞的浸润。组蛋白甲基化组蛋白H3在Lys27发生三甲基化（H3K27me3），在DNA低甲基化的背景下，抑制编码MHC-I、CXCL9和CXCL10的基因，并减少CD8+T细胞浸润。癌症细胞中的多梳抑制复合物2（PRC2）活性介导H3K27me3，抑制G-CSF和IL-6的表达并诱导性一氧化氮合成酶阳性（iNOS+）巨噬细胞、中性粒细胞和T细胞的招募。ARID1A是SWI/SNF复合物的DNA结合亚基, ARID1A的遗传突变会抑制IFN-γ诱导基因转录和CXCL9、CXCL10和CXCL11的表达，从而抑制T细胞浸润。组蛋白乙酰化组蛋白乙酰化通过组蛋白乙酰转移酶1（HAT1）调节免疫应答，HAT1介导CD274和组蛋白去乙酰化酶（HDACs）的表达，HDACs抑制编码PD-L1和PD-L2的基因的表达，以及CCL5、CXCL9和CXCL10的表达，从而损害T细胞浸润。细胞内信号通路癌症细胞的一个关键特征是异常信号传导，这是由编码或非编码DNA区域的遗传或表观遗传改变以及生长因子和代谢等外部信号驱动的。WNT–β-catenin通路激活的癌症细胞内固有WNT–β-catenin信号与免疫排斥相关，但其潜在机制仍不明确。WNT–β-catenin通过诱导ATF3表达阻断CD103+树突状细胞的募集和T细胞启动，并抑制CCL4或CCL5分泌。TGF-β通路淋巴细胞抗原LY6K和LY6E在癌症细胞中的过表达激活TGF-β信号转导和SMAD2/3磷酸化，导致NK细胞活性降低和Treg细胞浸润增强。αVβ6整合素的上调通过激活TGF-β和SMAD2/3磷酸化来调节SOX4的表达，SOX4抑制多种I型干扰素诱导基因和编码MHC-I的基因，同时增强PD-L1表达，这会损害细胞毒性T细胞介导的免疫。此外，癌症细胞衍生的TGF-β介导CD4+T细胞向Treg细胞的转化。而SMAD4的敲除会导致肿瘤细胞中TGF-β信号的失活，这增强了CCL9的表达和未成熟髓系细胞的募集。NF-κB信号通路PD-L1的表达受癌症细胞内NF-κB信号转导的转录调控，其途径是通过MUC1-c与CD274启动子上的NF-κB亚基p65的直接结合，或通过TGF-β介导的MRTFA的表达。MUC1-C和p65还驱动ZEB1转录，这导致编码CCL2、IFN-γ、GM-CSF和TLR9的基因受到抑制，CD8+T细胞浸润受损。相反，免疫疗法和化疗诱导的NF-κB和p300–CBP的活化增强了MHC-I抗原呈递和CD8+T细胞识别。HIF信号通路缺氧诱导的缺氧诱导因子1α（HIF1α）信号通路通过增强CD274的表达和抑制编码CCL2和CCL5的基因来驱动免疫逃避，这会损害NK细胞和CD8+T细胞的浸润，而HIF2α诱导CXCL1的表达并促进肿瘤的中性粒细胞募集。代谢改变在肿瘤的发生和进展过程中，癌症细胞及其TME不断暴露在恶劣的条件和压力下。为了生存和维持生长，需要细胞适应和代谢重编程。癌症代谢重编程与免疫抑制和逃避有关。肿瘤细胞消耗大量葡萄糖，这与T细胞浸润不良有关。高葡萄糖消耗导致乳酸的产生并分泌到肿瘤微环境中，乳酸在肿瘤微环境中以免疫抑制的方式起作用，降低NK细胞的细胞溶解活性，并增强PD-1表达和Treg细胞的免疫抑制能力。此外，乳酸增加了肿瘤和脾脏中MDSC的频率，并在肿瘤相关巨噬细胞中诱导“M2样”极化。此外，癌症细胞中谷氨酰胺的消耗降低CD8+T细胞的活化和浸润，并通过增加G-CSF的分泌增强MDSC的募集。小结近年来，关于癌症细胞-免疫细胞串扰的研究大幅增长。跨越式发展的技术进步，包括单细胞多组学技术，基于人工智能的系统生物学方法以及体内体细胞基因编辑策略，有望加速这些研究的深入，并最终形成针对个体患者的新型免疫干预策略。展望未来，个性化免疫干预策略将需要综合多组学肿瘤表征、免疫分析、液体活检样本的动态监测以评估基于血液的生物标志物、计算数据分析和转化，以及临床相关体外和体内模型的机制研究。有关癌症细胞内在和癌症细胞外部特征的知识体系将共同决定局部和系统免疫状况，以指导临床决策，并为针对患者个性化新型治疗方法开辟道路。参考文献：1.Mechanisms driving the immunoregulatory function of cancer cells. Nat Rev Cancer.2023 Jan 30.封面图来源：pixabay版权声明/免责声明本文为授权转载文章。本文仅作信息交流之目的，不提供任何商用、医用、投资用建议。文中图片、视频、字体、音乐等素材或为药时代购买的授权正版作品，或来自微信公共图片库，或取自公司官网/网络，部分素材根据CC0协议使用，版权归拥有者，药时代尽力注明来源。如有任何问题，请与我们联系。衷心感谢!药时代官方网站:www.drugtimes.cn联系方式:电话:13651980212微信:27674131邮箱:contact@drugtimes.cn股价大涨270%！涨的是所有GLP-1“弯道超车”的机会3个月减重近30斤，股价翻倍，Viking已被礼来看中？14.6亿美元！诺和诺德又有新交易点击这里，欣赏更多精彩内容！

蛋白质是生命过程中的重要执行者，从酶的催化到免疫的调控，都离不开蛋白质的参与。高通量蛋白质测序技术的发展为生物医学和临床研究提供了全新的视角和丰富的数据，进一步与人工智能算法的交叉融合，为解析疾病发生发展机制以及临床诊疗实践提供前所未有的契机。2023年11月28日，复旦大学类脑智能科学与技术研究院程炜研究员、冯建峰教授团队与附属华山医院郁金泰教授团队合作，于期刊Nature Communications发表题为"Plasma proteomic profiles predict individual future health risk"的研究论文。基于血浆蛋白质组学数据和人工智能算法研发了全新的蛋白风险评分，实现对未来多种疾病及死亡等重大健康结局的预测，并验证其在临床应用中的价值。研究结果支持血浆蛋白质组学数据的广阔应用前景，对疾病早期预警与精准防控具有重要意义。图1. 该课题研究思路研究团队基于英国生物样本库(UK Biobank)队列数据，对其45种常见疾病及死亡进行了中位数时长超14年的追踪随访。其中45种结局涵盖国际疾病分类第十版中的(International Classification of Disease 10th Revision, ICD-10)全因死亡和全部系统性疾病，包括感染性、血液、内分泌、精神、神经、感官、循环、呼吸、消化、皮肤、肌肉骨骼、泌尿生殖和癌症等重大健康结局。研究采用迄今为止全球最大的蛋白质组学数据(52,006被试)，通过对OLINK平台检测的1,461种血浆蛋白质测序数据进行建模分析，开发了一款全新的基于神经网络的蛋白风险评分(Proteomic Risk Score, ProRS)，该评分对呼吸系统疾病、循环系统疾病、糖尿病、肺癌、前列腺癌预测效能超85%，对全因死亡、痴呆、肾衰竭、肥胖、慢性肺栓塞预测效能超80%。共同通讯作者，复旦大学附属华山医院郁金泰教授表示：“既往针对蛋白质组学的研究往往聚焦于研究人员各自感兴趣的某类疾病，且更多局限于小样本横断面的病例-对照研究；该研究首次采用超5万例个体的血浆蛋白质数据并探究其对多种纵向健康终点事件的预测效用，是一项开创性的工作。”图2. ProRS对各疾病及死亡的关联散点图和风险分层图研究团队发现，高水平ProRS个体相较于低水平个体，其所有系统性疾病的发生率和全因死亡率均显著升高，且ProRS与年龄和性别显著相关。将ProRS三等分把人群划分为高、中、低ProRS人群后，可观测到高ProRS人群，其16种疾病和4种特定原因死亡的发生率较低ProRS人群增加超过5倍，特别是在呼吸系统及循环系统引起的死亡、痴呆、糖尿病和肥胖中，其风险超过20倍。这表明ProRS和整体健康密切相关。图3. ProRS对比不同预测因子组合该研究还深入探讨了ProRS的预测能力。单独使用ProRS预测时，Cox比例风险回归模型在10个结局中得到的Harrell C指数超过0.8。在绝大部分结局中，单独ProRS的预测表现较常用的三组预测因子相当或更好，包括年龄+性别、血清指标(SERUM,25个指标)或临床因子组合(PANEL,54个指标)；且在一部分结局中，单独ProRS的表现显著优于血清指标和ProRS的组合，说明ProRS包含有更丰富的预测信息。图4. ProRS的校准曲线和决策曲线进一步，该研究评估了预测模型的临床效用，采用了模型校准和决策曲线分析。不同预测指标组合都显示出良好的校准。在决策曲线分析中，和常用的两组预测因子相比(年龄+性别、临床因子组合)，仅基于ProRS的模型在大多数疾病中显示出更大的净效益。在常用的两组预测因子基础上，添加ProRS可以显著观测到临床效用的提升，且与单独使用ProRS表现相近。在特定的决策阈值范围内，ProRS甚至比不同预测因子组合表现出更高的净效益，说明ProRS的具有较好的临床应用潜在价值。“本研究的结果非常令人兴奋，我们发现蛋白质组学数据可以对多种疾病和死亡风险具有很好的预测效能，并且在相当一部分疾病中蛋白质的预测表现显著优于既往临床预测指标。这一结果证明蛋白质组学是未来精准医疗的绝佳切入点，让人对其未来的应用前景充满希望，一滴血预测未来疾病风险或将真的实现。”共同通讯作者，复旦类脑智能科学与技术研究院程炜研究员表示。图5. 蛋白在系统性健康疾病预测中的作用最后，该研究团队还通过夏普值(SAHP value)对血浆蛋白重要度进行排序，结果表明一些蛋白质在预测多种系统疾病及死亡结局中都发挥着至关重要的作用。例如，在对所有系统疾病及全因死亡的预测中，GDF15的重要度都位居前1%。此外CDCP1、CXCL17、EDA2R和HAVCR1在超过10个系统疾病中也均表现出重要的预测价值。随后，研究团队重点展示了癌症和痴呆两种备受关注的疾病。以癌症为例，较高的血浆CXCL14、GDF15、HAVCR1、CDCP1、TSPAN1、LTBP2和ACTA2可能会导致癌症风险升高，而较高的血浆RET水平显示出保护作用。对于痴呆，NEFL、BCAN、GFAP和GDF15是影响疾病风险的主要蛋白质，ACTA2、LTBP2和NCS1是新发现的与痴呆风险相关的重要蛋白。这些标志物为疾病早期识别和治疗干预提供了新的潜在靶点，也为临床实验和研究提供新的方向。复旦类脑智能科学与技术研究院冯建峰教授表示：“本研究中发现的重要蛋白对相关疾病的预防策略和治疗措施提供了重要靶点。未来课题组将聚焦于如阿尔茨海默病、帕金森病、抑郁、焦虑等与脑相关的重大疾病，进一步围绕蛋白质组学数据与神经精神疾病的关系开展研究，发掘新的蛋白标志物并探究其内在机制，为脑疾病早期诊断、预测和进展评估奠定理论基础。”此外，冯建峰教授补充道：“研究院也在积极推动脑体检项目，希望将多年积累的脑科学研究成果应用到老百姓体检中；相信不远的将来，大家都可以享受到国家对于我们科研投入带来的回报。”复旦大学类脑智能科学与技术研究院的博士后尤佳、复旦大学附属华山医院博士生郭钰、张一为共同第一作者。复旦大学类脑智能科学与技术研究院冯建峰教授、程炜研究员、复旦大学附属华山医院郁金泰教授为论文共同通讯作者。注：文中插图源于 Nature Communications原文链接：https://doi-org.libproxy1.nus.edu.sg/10.1038/s41467-023-43575-7本文仅用于学术分享，转载请注明出处。若有侵权，请联系微信：bioonSir 删除或修改！

Pictured: A DNA strand and a protein/Getty Earlier this month, Nature published results from a large-scale study of genetic variation in proteins related to human health. Rather than originating at a university or with the National Institutes of Health, the study came from a consortium of pharmaceutical companies. The Pharma Proteomics Project (PPP) was first announced in 2020 and has been described by participants as “one of the biggest industry consortia of its kind.” Pharmaceutical companies partnered with the UK Biobank, a repository of environmental, lifestyle and genetic data based in the United Kingdom, and Olink Proteomics to analyze circulating proteins in more than 50,000 biomedical samples. The team looked at proteins related to cardiometabolic function, inflammation, neurology, oncology and COVID-19 outcomes. Christopher Whelan Pre-competitive collaborations like the PPP are not uncommon. The Pistoia Alliance formed in 2007 to foster innovation in R&D and now has over 100 members globally. In 2011, Pfizer stated it had become part of multiple precompetitive consortia. BioSpace spoke with project leader Christopher Whelan, a director on the Data Science and Digital Health team at Johnson & Johnson Innovative Medicine, about how the team came together and its findings. Responses are lightly edited for clarity. BSp: How did the PPP come to focus on proteomics versus any other genomic technology? CW: The consortium primarily [consists] of geneticists. . . . And I think there’s increasing recognition that genetics gives us the blueprints of diseases, but we need to go beyond genetics to get the whole picture. So we came together to essentially translate the language of the human genome through proteomics. Proteins are the end products of genes, and they represent the majority of our drug targets and a large proportion of our circulating biomarkers. So the idea would be, can we tie together the vast amount of knowledge that we have gathered on the human genome with a larger body of evidence on circulating biomarkers and on our drug targets? BSp: How did you all decide to come together as a collective to do this rather than [just] one company funding the study? CW: A couple of explanations there. The first is that we built upon some preexisting consortia. There are, for example, two existing consortia focused on exome sequencing of the UK Biobank, and there’s also another consortium focused on whole genome sequencing of the UK Biobank. So there was precedent for pre-competitive pharma collaboration, especially around the UK Biobank. I think the second component would be [that] proteomics is a very exciting technology. It’s a new technology, and like a lot of new technologies, [it] can be expensive. So the idea was to come together and cost share, and by cost-sharing we were able to do something on a population scale that no one company could achieve by itself. BSp: The press release from 2020 said there were 10 companies, and when the final study came out there were 13. Is the consortium going to continue to expand? CW: That would be our hope. As you highlighted, we were 10 companies [J&J, Amgen, AstraZeneca, Biogen, Bristol Myers Squibb, Genentech, GSK, Pfizer, Regeneron and Takeda] when we began. We grew to 13. Alnylam, Novo Nordisk and Calico joined after that announcement. We have received interest from other companies. On joining, we intentionally placed sort of a hard stop or a barrier just so we could get this initial project and 55,000 samples underway. But now that that project is complete, we are sitting down as a group and considering what our next steps would be. And I definitely think that there will be openness for additional companies to join the next phase. BSp: What was something that was challenging about this process, whether on the front of getting all these people to work together or dealing with the actual research? CW: I think the contractual process was very challenging, just dealing with the legal components and all of the red tape that might be involved in getting 13 companies to agree on how we should do this. On the scientific end, it was actually quite a pleasant process. I think the vast majority of the companies were very eager to work together in a truly precompetitive, truly collaborative manner. So scientifically, [a] very, very, very, very positive experience. Just logistically, it can be challenging, but there are 13 companies all working towards a common goal. You’ll see in the paper that we aim towards the lower hanging fruits in conducting [genome-wide association studies] and doing some basic cross-sectional regressions of the most prevalent diseases. We all agreed that it made sense for us to do that together. There was an implicit acknowledgment that if we didn’t do it together, each of the 13 companies would go away and do their own version of the same analysis, and that can be very confusing for the scientific community. BSp: Do you primarily hope to use the data from this study for developing new molecules or repurposing existing drugs? CW: It’s a dataset with multiple high-impact use cases. Certainly, finding new targets is one. Validating some of our existing targets is a second. Repurposing of targets could be another opportunity. Beyond drug targets, I think in the future there could be opportunities to use this dataset to develop new diagnostics—you know, multiprotein markers that may be predictive of a disease or predictive of drug response. We are interested in using this data set to find subtypes of a certain disease. So for example, different biological subtypes of Alzheimer’s disease or major depressive disorder, we can conduct advanced [machine learning] to find those subtypes and then align those subtypes to the mechanisms of action of the drugs that are in our portfolios. BSp: Can you summarize some of the study’s more detailed findings? CW: First, we found thousands of associations between proteins and demographic factors like BMI, liver and kidney functions and the top 20 prevalent illnesses in UK Biobank. Some of the more notable associations included increased levels of inflammatory cytokines like CXCL17 in depression. We also saw broad upregulation of a protein called GDF15 across 18 of those 20 prevalent illnesses, and that would mean that’s a stress response cytokine that’s induced after injury. And our study would suggest that it could be a marker of general health, as it’s strongly upregulated across so many unique health conditions. Next, we found that proteins in combination do an excellent job of predicting age and sex and BMI, and liver function and kidney function. And then next, perhaps most notably, we built the world's largest pQTL [protein quantitative trait loci] library, or more simply, associations between gene variants and proteins. So we built a collection of over 14,000 associations between commonly occurring gene variants and protein levels. And then we have a few exemplars of why that proteogenomic library is important. We show how it helps us gain more insights into complex biological networks like the complement system, and that there’s a combination of short- and long-range interactions within the complement system. We also briefly touch upon the utility of the proteogenomic library to find causal associations between proteins and diseases. Those causal associations could potentially flag more powerful drug targets. The example that we give is [the enzyme] PCSK9 and lipids just because it’s a ubiquitous example—it’s been talked about quite a bit in the literature already. We recapitulate the causal relationship between PCSK9 and lipids and atherosclerosis, just as an example of how this dataset could be useful to find new drug targets. We don’t talk about new drug targets . . . but there will be papers in the future that look at that more systematically. BSp: What do you think the public should take away from this? CW: I think that the study is helping us translate the language of the human genome. It’s helping us translate our own instruction books. Basically, we’ve provided a foundation to find new drug targets, we’ve provided a foundation to build new blood tests that could screen for diseases or predict . . . your response to a particular therapeutic. The paper mainly describes how we’ve built that foundation. And we have touched upon some of the exciting things you can do with this dataset. But over the next six to 12 months, I think we’re going to see papers that are as high-impact if not more high-impact based on the foundation that we built. One thing we didn’t touch upon in the paper was the use of advanced [artificial intelligence] on this data set; there is a lot of untapped potential there and it’s something we’re absolutely exploring further within the data sciences group at J&J. We have such big data [that] we can use artificial intelligence and machine learning to uncover some of these paths that will help us better understand disease and finding targets. Nadia Bey is a freelance writer from North Carolina. She can be reached at beynadiaa@gmail.com.